"What if we're insufficiently optimistic?" Anish Acharya's case in eight parts, from a conversation with Jen Kha on AI: 1. Macro (03:34) - Prices for last-gen GPUs are RISING on a per-hour basis. That never happens in computing. It points to essentially infinite demand for intelligence and highly constrained supply. 2. Moats (05:30) - Network effects, scale, and brand are as good as they've ever been. "No amount of coding agents is gonna make Nike not Nike." The vulnerability is integration, the moat built on being painful to migrate away from. 3. Tokens (06:42) - For unbounded-upside work like sales and product, it's rational to pay almost any price for a model even one IQ point smarter. 4. Labs (10:37) - They're vertically integrating down into inference, not up into apps. Inference is one homogeneous workload at enormous scale. The app layer is a thousand idiosyncrasies of pricing and packaging. 5. Models (11:15) - They're not commodities. Some are literal and precise, some are creative, and organizations will need both. 6. Agents (15:20) - An agent is just a model in a loop with tools and memory. Coding loops already fix reported bugs end to end. Business loops come next, think the model that says "We need to open a branch in Tijuana." 7. Consumer (17:20) - We're in the DOS era of AI, so there's no app store for it yet. But the 99-cent app era is over, people are paying $200 a month, and the Birkin bag of software is coming. 8. Builders (35:05) - New business formation is skyrocketing. The 25-year-old who would've been a YouTube creator is now building software for their neighborhood: a mom-and-pop SaaS economy. YouTube: @illscience @jkhamehl